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Automated Classification of Issue Reports from a Software Issue Tracker

  • Nitish Pandey
  • Abir Hudait
  • Debarshi Kumar Sanyal
  • Amitava Sen
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 518)

Abstract

Software issue trackers are used by software users and developers to submit bug reports and various other change requests and track them till they are finally closed. However, it is common for submitters to misclassify an improvement request as a bug and vice versa. Hence, it is extremely useful to have an automated classification mechanism for the submitted reports. In this paper we explore how different classifiers might perform this task. We use datasets from the open-source projects HttpClient and Lucene. We apply naïve Bayes (NB), support vector machine (SVM), logistic regression (LR) and linear discriminant analysis (LDA) separately for classification and evaluate their relative performance in terms of precision, recall, F-measure and accuracy.

Keywords

Bug classification Naïve Bayes Support vector machine Precision Recall F-measure 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Nitish Pandey
    • 1
  • Abir Hudait
    • 1
  • Debarshi Kumar Sanyal
    • 1
  • Amitava Sen
    • 2
  1. 1.School of Computer EngineeringKIIT UniversityBhubaneswarIndia
  2. 2.Dr. Sudhir Chandra Sur Degree Engineering CollegeKolkataIndia

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